Wavelet-based techniques for classification of power quality disturbances

TL;DRAbstract

The quality of power supply has become an important issue for electricity utilities and their customers. In recent years there has been a rising incidence of damage attributed to the power quality supplied to the customers of electric utilities. Meanwhile, there has been a rapid increase in the already widespread use of electronic equipment and modem power electronic devices. These trends have both decreased the quality of power on the electric grid and increased the equipment's sensitivity to power quality disturbances. In order to improve the quality of the power supply, identifying the type and source of troublesome disturbances is an essential task. Existing automatic disturbance classification methods have replaced the traditional visual inspection of the disturbance waveforms. However, they are not reliable because those methods rely on the classification capability of large neural networks operating on inputs derived by simply pre-processing the disturbance signals with discrete

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